Scale-invariant anomaly detection with multiscale group-sparse models

Carrera, Diego; Boracchi, Giacomo; Foi, Alessandro; Wohlberg, Brendt

doi:10.1109/icip.2016.7533089

Cited by 16 publications

(21 citation statements)

References 20 publications

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“…They improve upon previous results by Carrera et al (2017), who build a dictionary that yields a sparse representation of the normal data. Similar approaches using sparse representations for novelty detection are (Boracchi et al, 2014;Carrera et al, 2015Carrera et al, , 2016. Schlegl et al (2017) train a GAN on optical coherence tomography images of the retina and detect anomalies such as retinal fluid by searching for a latent sample that minimizes the per-pixel 2 -reconstruction error as well as a discriminator loss.…”

Section: Related Workmentioning

confidence: 99%

Improving Unsupervised Defect Segmentation by Applying Structural Similarity to Autoencoders

Bergmann

Löwe

Fauser

et al. 2019

Proceedings of the 14th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Application

304

230

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Convolutional autoencoders have emerged as popular methods for unsupervised defect segmentation on image data. Most commonly, this task is performed by thresholding a per-pixel reconstruction error based on an p -distance. This procedure, however, leads to large residuals whenever the reconstruction includes slight localization inaccuracies around edges. It also fails to reveal defective regions that have been visually altered when intensity values stay roughly consistent. We show that these problems prevent these approaches from being applied to complex real-world scenarios and that they cannot be easily avoided by employing more elaborate architectures such as variational or feature matching autoencoders. We propose to use a perceptual loss function based on structural similarity that examines inter-dependencies between local image regions, taking into account luminance, contrast, and structural information, instead of simply comparing single pixel values. It achieves significant performance gains on a challenging real-world dataset of nanofibrous materials and a novel dataset of two woven fabrics over state-of-the-art approaches for unsupervised defect segmentation that use per-pixel reconstruction error metrics.

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Section: Related Workmentioning

confidence: 99%

Improving Unsupervised Defect Segmentation by Applying Structural Similarity to Autoencoders

Bergmann

Löwe

Fauser

et al. 2019

Proceedings of the 14th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Application

304

230

View full text Add to dashboard Cite

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“…As typical in the anomaly-detection context, anomalies are then detected as patches corresponding to outliers with respect to the features distribution [22]. Another reason for using [19] is that this algorithm can be extended to operate at different magnification levels [23].…”

Section: Introductionmentioning

confidence: 99%

Defect Detection in SEM Images of Nanofibrous Materials

Carrera

Manganini

Boracchi

et al. 2017

IEEE Trans. Ind. Inf.

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111

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Nanoproducts represent a potential growing sector and nanofibrous materials are widely requested in industrial, medical, and environmental applications. Unfortunately, the production processes at the nanoscale are difficult to control and nanoproducts often exhibit localized defects that impair their functional properties. Therefore, defect detection is a particularly important feature in smart-manufacturing systems to raise alerts as soon as defects exceed a given tolerance level and to design production processes that both optimize the physical properties and control the defectiveness of the produced materials. Here, we present a novel solution to detect defects in nanofibrous materials by analyzing scanning electron microscope images. We employ an algorithm that learns, during a training phase, a model yielding sparse representations of the structures that characterize correctly produced nanofiborus materials. Defects are then detected by analyzing each patch of an input image and extracting features that quantitatively assess whether the patch conforms or not to the learned model. The proposed solution has been successfully validated over 45 images acquired from samples produced by a prototype electrospinning machine. The low computational times indicate that the proposed solution can be effectively adopted in a monitoring system for industrial productio

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“…5.5.1. Our experiments [7] show that employing a multiscale approach in all phases of the algorithm (dictionary learning, sparse coding and anomaly indicators) achieves good anomaly detection performance even when training and test images are acquired at different scales.…”

Section: Multiscale Anomaly-detectionmentioning

confidence: 91%

Learning and Adaptation to Detect Changes and Anomalies in High-Dimensional Data

Carrera

2019

Special Topics in Information Technology

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The problem of monitoring a datastream and detecting whether the data generating process changes from normal to novel and possibly anomalous conditions has relevant applications in many real scenarios, such as health monitoring and quality inspection of industrial processes. A general approach often adopted in the literature is to learn a model to describe normal data and detect as anomalous those data that do not conform to the learned model. However, several challenges have to be addressed to make this approach effective in real world scenarios, where acquired data are often characterized by high dimension and feature complex structures (such as signals and images). We address this problem from two perspectives corresponding to different modeling assumptions on the data-generating process. At first, we model data as realization of random vectors, as it is customary in the statistical literature. In this settings we focus on the change detection problem, where the goal is to detect whether the datastream permanently departs from normal conditions. We theoretically prove the intrinsic difficulty of this problem when the data dimension increases and propose a novel non-parametric and multivariate change-detection algorithm. In the second part, we focus on data having complex structure and we adopt dictionaries yielding sparse representations to model normal data. We propose novel algorithms to detect anomalies in such datastreams and to adapt the learned model when the process generating normal data changes.

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Scale-invariant anomaly detection with multiscale group-sparse models

Cited by 16 publications

References 20 publications

Improving Unsupervised Defect Segmentation by Applying Structural Similarity to Autoencoders

Improving Unsupervised Defect Segmentation by Applying Structural Similarity to Autoencoders

Defect Detection in SEM Images of Nanofibrous Materials

Learning and Adaptation to Detect Changes and Anomalies in High-Dimensional Data

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